A closer look at labour market status and crime among a general population sample of young men and women

Journal Pre-proof A closer look at labour market status and crime among a general population sample of young men and women Anke Ramakers, Mikko Aaltonen, Pekka Martikainen

PII:

S1040-2608(19)30187-X

DOI:

https://doi.org/10.1016/j.alcr.2019.100322

Reference:

ALCR 100322

To appear in:

Advances in Life Course Research

Received Date:

18 February 2019

Revised Date:

11 October 2019

Accepted Date:

22 November 2019

Please cite this article as: Ramakers A, Aaltonen M, Martikainen P, A closer look at labour market status and crime among a general population sample of young men and women, Advances in Life Course Research (2019), doi: https://doi.org/10.1016/j.alcr.2019.100322

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A closer look at labour market status and crime among a general population sample of young men and women

Author information

Corresponding author Dr. Anke Ramakers Institute for Criminal Law and Criminology

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Leiden university [email protected] Steenschuur 25

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2311 ES Leiden

Dr. Mikko Aaltonen

University of Helsinki

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Prof. Pekka Martikainen

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Institute of Criminology and Legal Policy

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The Netherlands

Faculty of Social Sciences

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Abstract

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University of Helsinki

Those in the most criminally active age groups are facing particular difficulties in entering the labour market and accumulating stable work experiences. This study uses a large representative sample of Finnish adolescents to examine how different labour market statuses are associated with crime. Both for men and women, within-individual variation in employment is inversely linked to all crime measures considered, albeit to a different extent. In addition, qualitatively different categories of non-

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employment (e.g., non-participation without legitimate reason, studying, being on parental leave) are distinctly associated with crime. The findings underscore the importance of a detailed conceptualization of labour market status in research that aligns with the changed nature of employment and approximates the actual labour market experiences of young adults.

Keywords: Crime, labour market status, employment, unemployment, non-participation, gender

Introduction

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The economic crisis weakened the labour market position of young people more than other age groups (Caliendo and Schmidl 2016; Carcillo et al. 2015). Their weak labour market attachment is reflected in the high share of inactive youth (i.e., NEETs: not in employment, education, or training) and their

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overrepresentation in nonstandard temporary jobs (Carcillo et al. 2015). A lack of access to

(meaningful) employment in early adulthood can increase the risk of exclusion and criminal behaviour

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(MacDonald 1997; Sampson and Laub 1993). Currently, little knowledge exists on how the diverse types of employment and non-employment as experienced by youth – the most criminally prone age

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group - can lead them towards crime.

Instead, the bulk of research on the work-crime relationship has focused on the effect of being

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employed, lumping together different types of employment and non-employment. Recently, an increasing number of studies has provided evidence for the moderating role of the characteristics of

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employment, nuancing the notion that any job protects against crime (Horney and Apel 2017; Loughran, Nagin and Nguyen 2016; XXXX 2017; Sampson & Laub 1993; Uggen 1999, 2000; Van

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der Geest, Bijleveld and Blokland 2011; Wadsworth 2006). Like the “employed category”, the “not employed-category” can house a heterogeneous group of individuals, including individuals who are searching for a job (the unemployed) and individuals who are not searching for a job with(out) legitimate reasons (non-participants). Not working can thus mean qualitatively different things depending on the type of non-employment, and presumably can have different effects on crime. Yet, methodologically rigorous research that pays attention to the variety of non-employment is scarce (XXXX et al. 2013; Bennet and Ouazad 2016). Existing studies either focused solely on official 2

unemployment and excluded other non-employment statuses or used a heterogeneous nonemployment category. Recently, Kleck and Jackson (2016) drew specific attention to how different kinds of nonemployment could have a different impact on crime, and articulated the need for a clearer conceptualization of non-employment in work-crime studies. In their retrospective case-control design, employment status and sociodemographic information was used to predict individuals’ membership of either the case group (a sample of prisoners sentenced for serious property offences) or the control group (a nationally representative sample of the U.S. population). They concluded that

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“joblessness itself does not increase serious property offending” (p.508) and found that not

unemployment or underemployment, but being out of the labour force without legitimate reasons was positively associated with crime. Though informative, the findings of this cross-sectional study are

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possibly compromised by selection bias and cannot be generalized to other populations. Prospective studies in general population samples are warranted to examine whether and how different types of

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both employment and non-employment serve as causal factors that push individuals towards crime. The current study addresses this omission by using annual panel data on all young individuals

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born between 1986-2000 who lived in Finland in 2000 (ages 18-26 years) (N=317,320) to examine whether a wide array of labour market statuses predict criminal involvement. Three doses of

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employment (full year, 6-11 months, <6 months) and seven doses of non-employment (unemployed 612 months, unemployed <6 months, non-participant without legitimate reasons and non-participant for

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legitimate reasons (student, disability pension1, military, on parental leave) will be examined. An important contribution lies in this study’s ability to perform fixed-effects models which

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greatly reduce the chance that the studied relationships are driven by selection bias. Fixed-effects models address selection into employment outcomes by absorbing the variance caused by stable between-individual differences. What remains are the within-individual differences in both employment and crime, which are the points of interest in our study (Allison 2009).

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Intellectual or physical disabilities are fairly common causes of disability retirement in young adulthood, but mental health problems (schizophrenia, depression) are also common reasons for receiving pension during these ages (Finnish Centre for Pensions 2017). Note that many transition from disability pension back to the labour force when health improves.

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A second contribution is that we not only distinguish between theoretically relevant employment measures but also examine subsamples while retaining statistical power. For instance, as both employment and crime are highly gendered, the relationships under study might differ for young men and women. Thus far, most empirical research on crime and desistance is based on all male or male-dominated samples. Despite the vast contributions to gender and crime (see Kruttschnitt 2013, for a review of the literature), consensus on the existence of gender-specific effects is lacking, specifically for serious crimes - the domain of criminality where gender differences are greatest (Steffensmeier and Allan 1996). Also, the literature suggests that the effects of (non-)employment may

violent and property crime in addition to the omnibus measure.

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vary across crime types (Agnew 1992; Becker 1968), which is why we estimate separate models for

Third, given the dominance of American research in the field of work and crime, Finland

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represents a different context for examining these associations. Major distinctions are the low levels of income- and gender inequality in Nordic countries. For instance, the redistributive social welfare

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system makes the lack of employment less of a financial concern and possibly lowers the relative gains from (property) crime. Also, the labour force participation rate of women and men is fairly equal

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within the age groups considered here (Statistics Finland 2018), which possibly makes the meaning of employment more comparable across gender. The effect estimates in this study may therefore provide

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a lower bound for the potential effects in less egalitarian countries. Theory on labour market status and crime

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All theories discussed below suggest that individuals are less likely to commit a crime while employed versus being out of work. Importantly, close reading of these theories suggests that diverse

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employment- and non-employment doses can have different effects on crime. Hirschi’s (1969) social control theory assumes that individuals will engage in delinquent

behaviour in the absence of close relationships with conventional others. Conventional relationships with co-workers or employers socialize individuals to obey the dominant law-abiding norms and values. Sampson and Laub (1993) emphasize the salience of such adult bonds, also referred to as ’turning points’, for changes in crime during the life course. Their age-graded theory of informal social control posits that employment (and other adult bonds) can protect from crime through the 4

accumulation of conventional ties that accompany steady employment. What follows from Sampson and Laub’s theory is that changes in social embeddedness between both doses of employment and doses of non-employment can help to explain criminal behaviour. For instance, because an individual is embedded in conventional life during unemployment by trying to find work, the risk of crime will be lower during unemployment than during time spent outside the labour force without legitimate reason. Moreover, the risk of crime will be relatively low during time spent on parental leave, as ties to family reflect another kind of social embeddedness. Though focusing on the importance of opportunities rather than social embeddedness, routine

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activity theory leads to similar expectations, placing the (stable) employed at the lowest risk for crime, followed by the unemployed and otherwise occupied non-participants and finally the group of non-

participants without legitimate reasons. This theory emphasizes that criminal behaviour relies on the

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presence of a motivated offender, suitable targets and absence of social control (Cohen and Felson 1979). Employment reduces the opportunities to commit crime, depending on job intensity and

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duration. This line of thinking relates to the incapacitating effect of participating in formal institutions. Employment could induce a direct incapacitation effect which leaves little time or opportunity for

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committing crime (Bratsberg et al. 2018; Fallesen et al. 2018). The same can be true for time spent otherwise occupied, such as being in the military or on parental leave. In the same vein,

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unemployment is expected to increase criminal behaviour less than non-participation without legitimate reasons because of the differential impact on the opportunity structure for crime. This seems

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especially relevant when eligibility for receiving unemployment benefits depends on fulfilling certain job-search requirements and taking part in active labour market programs, as is often the case in

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Finland. This kind of unemployment benefit scheme leaves one less time to engage in unstructured socializing. Indeed, several Nordic studies showed that the intensity of a labour market program for welfare recipients affects criminal behaviour (Andersen 2012 ; Fallesen et al. 2018). Strain theory leads to somewhat different expectations regarding the non-employment doses. According to this theory, criminal behaviour is the result of frustrations individuals feel when they are unable to reach the desired material and immaterial goals (Merton 1968; Agnew 1992). Stable employment assures an income and status which decreases strain and makes crime less necessary. 5

Unemployment can lead to crime because of frustrations related to an unsuccessful job search, while non-participation (with or without legitimate reasons) might be less likely to lead to frustrations or strain as individuals are then not looking for employment and have reconciled with their out-of-work status (Agnew 1992; Kleck and Jackson 2016, p. 491). Unlike the previously discussed theories, economic theories lead to comparable crime risks across non-employment doses and only pertain to crime for financial gain. Instrumental crime is expected to increase when the potential costs for this behaviour are lower than its potential benefits (Becker 1968). According to this theory, especially long-term employed individuals commit fewer

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crimes because they do not want to risk losing their job. Losing benefits can be seen as a potential cost of crime during times spent out of work. What follows is that differences in eligibility for benefits

across non-employment doses could predict crime outcomes. As the welfare system in Finland assures

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that all individuals, except those who truly live ‘off the grid’, are eligible for social support, this not only weakens the relative instrumental gains from crime but also results in comparable crime risks

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across non-employment doses. This expectation is further substantiated by the fact that the difference in money received with standard unemployment benefits (labour market subsidy or basic

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unemployment allowance) and last-resort social assistance is negligible in Finland.2 Moreover, there is some evidence to suggest that losing benefits is not even considered as a cost of crime. Bennett and

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Ouazad (2016) find strong positive effects on property crime in their study on job displacement after mass-layoff events in Denmark. Being a country with relatively high unemployment benefits, like

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Finland, this Danish finding suggests that benefits do not affect the risk estimation that individuals make according to the rational choice perspective.

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Prior research on labour market status and crime

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Social assistance can be paid to those outside the labour force who lack income or assets deemed sufficient to meet basic needs. Some of those who receive unemployment benefits also receive social assistance simultaneously (Raittila et al. 2018). Those individuals who have sufficient employment histories and belong to an unemployment fund can receive higher earnings-related unemployment allowances. In the examined age groups, however, receipt of the earnings-related benefit is still fairly uncommon.

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Few studies examined whether the work-crime relationship varies across doses of (non-)employment. Specific attention is paid to findings regarding the doses under investigation in the current study: duration of employment, unemployment and various types of non-participation. Empirical studies are mostly based on American offender samples and seem ambiguous concerning the effect of job duration. Sampson and Laub (1993) found that job stability (combination of employment situation, stability of most recent job, and work performances) reduced recidivism risks. Most recent studies based their measure of job stability on the duration of employment. Uggen (1999) did not find conclusive evidence for the crime-reducing effect of job duration (see also

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Wadsworth, 2006). Dutch longitudinal research among a young high-risk offender population did find support for the protective effect of job stability (Van den Berg, 2015; Van der Geest, Bijleveld and Blokland 2011; Verbruggen, Blokland, and Van der Geest, 2012). Loughran, Nagin and Nguyen

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(2016) examined how past participation was associated with later decisions and found that

accumulated legal employment decreased crime among young serious offenders. Using data on

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another sample of serious offenders, Apel and Horney (2017) concluded that subjective measures (reported job commitment) instead of objective measures (hours worked) were inversely related to

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crime.

Longitudinal research that differentiates between doses of non-employment is very scarce. 3

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Often, the term “unemployment” is falsely used to refer to all individuals who are not employed. These studies do not differentiate between non-participants who did not want to work, did not search

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for work because they were otherwise occupied (in school, on parental leave, retired), and those who could not work (disabled). In other studies, the focus on official unemployment is driven by data

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availability (unemployment insurance systems). Economists have commented on the use of these data by stating that ‘[because u]nemployment data exclude persons who have withdrawn from the labour

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Although not the primary focus of these prisoner studies, there have been other efforts to distinguish between different non-employment categories. For example, both Welsh (2007) and Makarios, Steiner and Travis (2010) tried to examine those unable to work as a separate non-employment category next to unemployment. While this category still included a rather mixed group of individuals, such as disabled, in-school-, retired and reinstitutionalized individuals, these studies further emphasize the relevance of a closer examination as different recidivism rates were found across categories.

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force for market-driven reasons, so-called "discouraged workers", these data miss a key part of the story’ (Murphey and Topel 1997: 295). To our knowledge only two criminological studies paid attention to the effect of multiple nonemployment doses. Kleck and Jackson (2016) differentiated between four types of non-employment (unemployment, part-time employment, out of the labour force for legitimate reasons, out of the labour force for not widely accepted reasons) in their cross-sectional case-control design in which they used labour market status to predict an individual’s membership of a prison sample (N=476; N=325) or general population sample (N=5582). Only those who were classified as being out of the labour

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force for not widely accepted reasons were found to be more likely to belong to a prisoner sample. As they selected only serious property offenders (burglars or robbers) from the prisoner sample, they presented this finding as evidence for a true positive association between non-participation for

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illegitimate reasons and the risk of committing a burglary or robbery. The unemployed and

underemployed seemed at similar risk of engaging in robbery, whereas individuals who did not

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participate in the labour market for legitimate reasons (retired, in school, keeping house) were significantly less likely to commit such a crime. The authors acknowledged that although they

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controlled for several potential confounders, longitudinal data is warranted to address selection bias and gain better insight into the causality of the work-crime relationship.4

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A longitudinal study from the Netherlands provides additional insight into the effects of different types of non-employment on crime. Using data on official serious offending among youth

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who spent time in a juvenile institution (N men=270; N women=270), Verbruggen et al. (2015) examined the effect of formal employment and income support on crime. They distinguished between

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an omnibus measure of income support, separate measures for unemployment benefits and two types of non-participation benefits (public assistance and disability benefits). Random and fixed-effect models showed that the direction of the relationship between not working and crime differed for men

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A related limitation is the static measurement of employment and crime. For the offender sample, employment status revealed the situation in the month prior arrest, a time which is likely to be affected by the illegal activities that led to the imprisonment. As for crime, no information is available for the control group. Instead, this measure is based on the assumption that those in the general population sample have not committed a serious property offence.

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and women and depended on the type of income support received. Income support was generally found to reduce serious offending for men, but did not affect women’s crime risk. For men, the negative effect of income support was the result of receiving public assistance and disability benefits. The null-effect of income support among women concealed the crime-reducing effects of unemployment insurance and public assistance combined with the somewhat surprising crimeincreasing effect of disability benefits. The patterns of findings were similar for the omnibus crime measure and property crime, but were weaker for violent crime. Though low in number, the discussed studies seem to emphasize the importance of a close

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examination of the employment situation over simply studying the effects of being employed.

Gender differences

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Men and women differ in many ways in terms of employment experiences and criminal involvement. Yet, popular life course theories offer general predictions and little guidance on whether labour market

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status affects crime across gendered lines. Some scholars suggest that female and male crime can be accounted for by the same factors and through similar mechanisms (see Smith and Paternoster 1987,

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for a review of the literature). For both sexes, crime responds to the same societal conditions, making a gender-specific criminological theory unnecessary. Instead, the gender gap in crime is the result of

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differential exposure to risk factors (i.e., opportunities). As social differences between men and women in labour force participation and family life decrease over time, the meaning that men and

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women attach to work becomes more similar, resulting in similar (non-)employment-effects on offending across gender.

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Others argue that female and male offending may be better understood by a gendered approach in which attention is paid to how gender contributes to gender differences in crime (see Steffensmeier and Allan 1996, for a review of the literature). Following that perspective, labour market status is believed to affect female and male crime to a different extent and through different mechanisms. For instance, the protective effect of employment could be stronger for men because employment remains more important for the male identity, whereas women often combine work with other conventional social roles (Lorber 2001). As such, the stigma of being unemployed or receiving 9

benefits may fall more heavily on men (Verbruggen et al. 2012). In the same vein, men might be more sensitive to financial incentives for committing crimes as they often remain to be the primary earner in a household (Denver, Siwach and Bushway 2017). On the other hand, the employment effect could also be stronger for women. As women are more socially oriented than men, they may be both instrumentally and socially tied to the workplace. Women may therefore be more attached to work and as a result may gain greater protection from work (or are more affected by job loss) than men, resulting in stronger work-crime associations among women. Also, females who are unemployed and single care-takers are more likely to have financial problems, which might motivate crimes for

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financial gain (Verbruggen et al. 2012).

The previously discussed study of Verbruggen et al. (2015) showed that most types of income support received by the jobless had similar inverse effects on crime for men and women. None of the

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other studies that directly compared employment estimates for men and women considered different types of (non-)employment. Instead, a dichotomous employment measure was used, comparing

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employed individuals to all those without employment. Most of these studies concluded that a job was more inversely related to male offending compared to female offending (Benda 2005; Cobbina et al.

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2012; De Li and MacKenzie 2003; Simons et al. 2002; Verbruggen et al. 2012) (for a review see Rodermond et al. 2016). This suggests that, also in recent years, work status is more important for the

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male than the female identity. This study

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The current study adds to the existing body of work by using a representative sample of young Finnish men and women and distinguishing between many different labour market statuses, which better

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approximate the actual continuum of employment as experienced among youth. Figures 1 and 2 show the gender-specific development of labour market status by age. These

figures highlight the fact that measuring employment with a single measure is rather complicated during young adulthood. At least in the Nordic context full-time employment is relatively uncommon during the most crime-prone years, and the category of non-employment is very heterogeneous at the beginning of the follow-up. At the same time, most of those non-employed are outside the labour force

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for legitimate reasons. This suggests that measuring either only employment or unofficial unemployment misses a large part of the total picture. Our review implies that most of the relationships under study received little attention thus far. Importantly, studies that did distinguish between types of employment or non-employment found evidence for heterogeneous effects on crime. Notably, these findings are merely based on small, nonrepresentative samples and a limited control for selection bias. Particularly relevant for the current study is also their use of an omnibus crime measure and male samples. Consequently, whether and how the effects of different labour market statuses on crime vary for crime type and for men and

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women remains an unanswered question.

As for crime type we expect to find stronger relationships for property crime compared to the omnibus and violent crime measure. Reflective of its relationship with self-sufficiency, the labour

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market status measure correlates strongly with annual taxable income. Those working for all 12 months have the highest mean income in all age groups, and by age 26, the annual income is on

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average about 30,000 euros. Annual taxable incomes among the unemployed and non-participants without legitimate reasons are much lower, between 5,800 and 7,800 euros by age 26 (results available

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from the authors).

As social differences between men and women are comparatively small in Finland, we expect

[Figure 1 & 2]

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Data

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to find similar associations for both groups.

We used annual panel data on all individuals born between 1986–1990 who lived in Finland in year

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2000 (n=317,320). Longitudinal population-, crime- and employment-registers of parents and children were linked by means of personal identification codes, which have been anonymized for research purposes (Statistics Finland permission TK-53-525-11). The follow-up spans ages 18-26 and years 2004-2016. After accounting for mortality (1,632 individuals) and excluding years spent abroad, the final number of observations is 2,819,719 (person years). [Table 1 about here] Labour Market Status 11

Our measure of labour market status comprises different varieties of employment and nonemployment. The basis of this time-varying variable is the measure “main type of activity”, compiled by Statistics Finland from a variety of different administrative registers. First, we created the categories indicating employment intensity. The reference category “employed full year” consists of individuals who were employed in each of the 12 months in a given year, whereas the other employment categories denote employment for 6-11 or 1-5 months. Importantly, the non-employment months in these categories can consist of unemployment, studying or being outside the labour force. Employment is thus used as the “overriding” criterion for these categorisations. As an example,

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somebody who worked for 5 months and was registered as unemployed for 3 months will be classified as being in employment for 1-5 months. During the entire follow-up, roughly 57 percent of personyears are classified as being employed (Table 1).

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After identifying those employed, we created the categories of unemployment. Importantly, these people are such who did not work at all during the year, but were registered as officially

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unemployed (looking for employment) for varying lengths. The first dose consists of individuals who were officially unemployed for 6-12 months, whereas those who searched for work between 1 and 5

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months belong to the next one. Finally, those who were neither working nor officially unemployed belong to the dose “non-participant without legitimate reasons”. Roughly five percent of person years

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are classified in one of these three categories.

The reason as to why unemployment rates appear rather low in this classification is the fact

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that a substantial part of unemployment months belongs to “overriding” categories of shorter employment (5% of total unemployment months), as explained above. On average, roughly 46-47

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percent of individuals in those categories spend at least one month unemployed during the year, the unemployment length being around one to two months. Thus, the distinctions between these categories of employment and unemployment are not clear-cut, and the created variables represent a continuum rather than discrete differences. Another reason as to why these unemployment rates appear lower than national unemployment statistics is that the latter are typically “snapshot” measures of labour market status during a specific moment or in one month, whereas our measure tracks the status during the

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whole year. As we give precedence to employment in the categorization, our non-employment doses reflect relatively persistent unemployment/non-employment. Finally, we are left with several categories of being outside the labour force for legitimate reasons. Roughly 29 percent of person years are spent in the status of student, a proportion which decreases heavily by the end of the follow-up as the study participants age (see Figure 1 and 2). Once again, only those individuals who did not work at all during a year are classified as students, but there are many more individuals who are both working and studying that belong to the employment categories. Roughly one percent represents time spent receiving a (disability) pension, while the

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remaining categories of (obligatory) military service (3%) and parental leave (6%) take up the rest of the variation. Crime

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The crime measures identify all police-recorded crimes committed by the sample members during the nine-year follow-up and avoid biases caused by self-report. Thus, these variables indicate that the

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person in question was suspected of an offence during a given year, and resemble arrest measures often used in criminology. We use three categories of offences: 1) all crime (excluding traffic

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offences), 2) property crime (“Offences against property”) and 3) violent crime (“Offences against life and health”), which are based on crime classifications used by Statistics Finland (2014). Most property

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crime offences are thefts, whereas violent crimes are mostly comprised of assaults. All three variables are coded as binary indicating whether or not the individual committed such crime during the year in

Method

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question.

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The primary analytical models in this study are fixed effects linear probability models (FELPM) (Allison 2009, Wooldridge 2002). These models examine within-individual variation and exclude individuals who do not experience change in the treatment variables during the follow-up. As such, this method differs from fixed effect logistic regression models –which are normally used for binary outcomes – in that it does not exclude individuals who do not vary in the outcome measure. Other advantages of FELPM are the comparability and the interpretability of estimates as they represent the

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mean marginal effects of treatment variables on the outcome (f.e., a marginal effect of 0.01 corresponds to a 1 percentage point increase).5 In all models labour market status and age are the sole predictors of crime. The vast majority of individuals experience a change in labour market status and all age. As such the effective sample size is large and similar across models.6 In addition to the FE models, we present basic descriptive statistics on the bivariate associations between labour market status and crime over the entire followup. Results

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Bivariate associations between labour market status and crime are shown in Table 1 and partly imply the existence of dose-response associations between employment and the different crime measures. When focusing on the first five listed doses and the omnibus crime measure, we find that crime

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chances increase when employment duration in a person year decreases (3.2%-20.9%). At the same time, person years spent outside the labour force do not fit within this pattern as we find low

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percentages of crime for the years spent in non-participation for a variety of qualitatively different (e.g. on parental leave: 3.2%; disability pension: 5.4%). More surprising is the relatively low

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percentage of crime that was found for person years spent in non-participation without legitimate reasons (9.2%).

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Table 2 presents results from a fixed effects LPM for the general within-individual association between labour market status and crime, controlling for age. This model largely corroborates the

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bivariate results, even though the associations appear weaker after controlling for stable betweenindividual differences. The same individual is more likely to commit a crime during a year when he or

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she is employed for a shorter duration when compared to a year of full employment. This probability seems to increase further during years of no employment and is highest during short-term

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An disadvantage of FELPM is that it may result in meaningless predicted probabilities (<0 or >1). This is a lesser concern as our interest lies in the magnitude of coefficients instead of in the predicted probability. The residual is heteroskedastic which is addressed using cluster-robust standard errors. To test the robustness of findings, we also ran non-linear models (fixed-effects logistic regression). This led to similar conclusions. 6 The fact that all individuals age means that everybody is technically included in the model. However, only the variation in labour market status contributes to the estimates for that variable. Altogether 312 085 individuals have more than one unique value in labor market status (they appear in at least two different labour market statuses in the data).

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unemployment. Compared to full employment, being unemployed for 1-5 months is associated with a 2.2 percent point increase in the probability of crime.7 [Table 2 about here] Unexpectedly, spending time as a non-participant without legitimate reasons, being completely “off-the-grid” in terms of employment and registered unemployment, does not increase the probability of crime compared to spending a full year in employment. Significant associations are found for some of the other non-participant categories. Time spent as a student is associated with a small increase in the probability of crime (b=0.003, p<0,001). Serving in the military and being on

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parental leave are associated with a 0.8 and 1.3 percentage point decrease in the probability of crime. [Table 3 about here]

Gender-specific logistic models for any crime are presented in Table 3, and they indicate

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roughly similar patterns of findings for both men and women. Focusing on the first five categories

(including the reference category), these estimates also suggest that the probability of crime increases

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when the attachment to work (i.e., employment duration in a person year) decreases. For men the estimates replicate the exact pattern found for the full sample; being unemployed for 1 to 5 months is

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associated with the highest probability of crime (b=0.018, p<0.001) compared to full employment. For women we find the highest probability of crime during years spent in unemployment for 6 to 12

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months (b=0.031, p<0.001). It should be noted however that the differences in estimates for the two unemployment categories are small and not significant.8 A more important difference is that,

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compared to working year-round, being ‘off-the-grid’ is associated with a 0.7 percentage point decrease in the probability of crime for men and a 0.7 percentage point increase for women.

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Moreover, years spent in military service decreases crime only for men (b=-0.014, p<0.001), while receiving disability pension increases crime for women only (b=0.012, p<0.001).

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The same model was run using fixed-effects logistic regression, and as a result only included those individuals who have variation over time in both labor market status and crime. Substantively the results are very similar to fixed effect linear probability models. The odds ratios for the unemployment categories are 1.37 and 1.38 (.020 and .022 for base models), and the variables with significant associations in the base models are also significant and have the same direction in the fixed-effects logistic models. Given that the mean of the outcome is around 0.047, the models are also largely in agreement in terms of relative differences between different statuses. 8 Additional models showed that the differences between the two unemployment categories were not significant. The differences between them and the three employment categories, on the other hand, are significant in all of these models.

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The models presented in Tables 4 and 5 inform us of the types of crime that drive the results. A quick glance at both tables reveals somewhat stronger estimates for property crime, suggesting that within-individual variations in property crime are the primary drivers. For men, being employed for 6 to 11 months does not increase the probability of property crime compared to working year-round. In addition, being “off-the-grid” is not associated with a decrease in this probability while we find a slightly stronger positive association for women (b=0.010, p<0.001) (compared to general crime measure). Otherwise, for both men and women the pattern uncovered with the omnibus measure is replicated.

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[Tables 4 and 5 about here]

The results are different for violent crime. Starting with the findings for men, Table 5 shows

that the probability of violent crime does not differ between the employment categories. We do find a

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small but significant increase in the probability of violent crime for short-term unemployment

(b=0.003, p<0.05) and long-term unemployment (b=0.005, p<0.01) compared to being employed 12

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months. Being non-participant without legitimate reason was not associated with property crime but is associated with a 0.8 decrease in the probability of committing a violent crime. Noteworthy is that

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time spent as a student increased the probability of committing a property crime but decreases the probability of a violent crime (b=-0.002, p<0,001).

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For women, the effects for violent crime seem to be more in resemblance with the two other crime measures though weaker. An important example of this is that time spent outside the labour

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force without legitimate reason increases the probability of any crime and property crime but shows no significant association with violent crime (b=0.002, p>0.05).

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Discussion

Despite the extensive focus on employment and crime in life-course criminology, most prior studies had used somewhat crude measures of either employment or unemployment, overlooking the heterogeneous nature of the non-employed category, especially during the transition to adulthood. Using a general population sample of Finnish young adults, we set out to examine whether more detailed and accurate distinctions between different (non-)employment experiences among both men and women could bring more insight into the work-crime relationship. 16

Our main findings indicate that the probability of crime increases rather systematically as the attachment to employment and the formal labour market grows thinner. The same individuals commit more crimes in years they spent partly employed compared to years in full-year employment, and the probability of crime is highest in years with unemployment spells. These findings are in line with those found in prior studies that observe an inverse association of job stability and various types of crime (Loughran et al. 2016; Sampson and Laub 1993; Verbruggen et al. 2012;2015). Distinct probabilities of crime compared to full employment were also found for several of the nonparticipation categories. While being on parental leave is generally associated with the lowest

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probability of crime, student statuses are typically associated with a slightly higher probability of

crime than full-year employment. Against expectations, the probability of crime was not consistently higher across models in years spent ‘off the grid ’(i.e. outside the labour force without legitimate

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reasons). While women in this non-employment dose were indeed more likely to commit a (property) crime than during full-year employment, men showed no positive associations for these two outcomes

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and were instead found less likely to commit a violent crime.

These findings suggest that it is important to use nuanced measures of labour market status

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that are able to differentiate between quantitatively -and qualitatively different categories of (non)employment. When examining crime among young adults, it is important to acknowledge that most

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of those who are not employed are studying, and conflating these statuses with unemployment is likely to underestimate the effects of unemployment on crime. In a sense our findings are in line with those

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of Kleck and Jackson’s cross-sectional study (2016). However, our results extend the previous findings by showing that these differences also emerge in within-individual models and thus suggest

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that stable between-individual differences cannot be the sole reason for these results. However, the large differences between bivariate and within-individual associations observed in our study also suggest that there are important selection mechanisms at play that are stable over time. Lack of selfcontrol is an example of such a stable characteristic that could explain both employment and crime (Gottfredson & Hirschi 1990) but is accounted for by the with-in individual analyses of the current study. It is however also possible that omitted time-varying variables (e.g. substance abuse, stressful life events) may explain both labour market positions and crime outcomes. Thus, even though our 17

method and detailed measurements reduced the potential for confounding significantly, the results cannot be given a firm causal interpretation. In line with all the discussed theories, we find that the probability of crime is lower in periods that include employment, is particularly uncommon in years characterized by full employment and lowest in periods spent on parental leave. According to the age-graded informal social control theory these differences in crime are related to differences in social embeddedness (Sampson and Laub 1993), while routine activity theory points to the differences in opportunities (f.e. incapacitation in employment) and lifestyle (Cohen and Felson 1979). Likewise, the finding that being a student

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increased the probability of crime, compared to working year-round, can be explained by both

mechanisms. Arguably, students are less embedded and have more free time to engage in crime than

those employed. They may also be more frustrated and have a lower income. Strain theory posits that

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differences in frustrations cause different crime risks (Agnew 1992) and economic theories link these outcomes to differences in financial cost-benefit analyses (Becker 1968). As multiple theories predict

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a similar pattern of findings and we were not able to directly measure the underlying mechanisms associated with these theories, future research that can accomplish this is warranted.

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Strain theory differs in the expectations regarding the crime-prone non-employment statuses (long-and short-term unemployment, non-participation without legitimate reasons), as it posits that

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unemployment increases crime more than non-participation through the frustrations related to wanting but not finding a job (Agnew 1992). Differences between these categories may therefore speak to the

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validity of opposing mechanisms. In line with strain theory, the probability of crime was significantly higher during periods of unemployment than during full employment, while time spent ‘off the grid’

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increased crime only for women and to a lesser extent. Yet, the differences between the three crimeprone non-employment doses were not statistically significant. For that reason, the findings may be most in line with economic theories that expect similar cost-benefit estimations among the unemployed and non-participants without legitimate reasons resulting in comparable crime risks across these three non-employment doses. Future research in other welfare regimes is warranted to examine to what extent this finding could be due to the fact that all individuals (unemployed or non-participant without legitimate reason) 18

are eligible for social assistance in Finland. Further examination of the data did indicate that nonparticipants are relatively less likely to receive such income.9 Still, it may be that in Finland the two states are financially too similar to one another to lead to a substantial difference in criminality. Another explanation for the lack of significant differences between the non-employment categories and, more specifically, the relatively low crime risk during periods spent ‘off the grid’ could be that it concerns a heterogeneous category, even in our analyses. A further analysis of the data indicated that these NEET-individuals may not be living ‘off the grid’. Instead, they are more likely to live with their parents and perhaps therefore do not apply for unemployment benefits or social

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assistance. So while we distinguished between several quantitatively and qualitatively different categories of (non-)employment, more detailed data is needed to identify those who are most

disadvantaged and less attached (to the labour market), as well as their living conditions. Relatedly,

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data on informal employment could help to corroborate our interpretation of the findings. Perhaps years spent outside the labour force without legitimate reasons are less risky because they in fact

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represent time spent working but in jobs that do not come to the attention in formal labour force statistics. The discussed growth in alternative nonstandard work arrangements in recent years includes

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the informal economy (Bracha and Burke 2016). Especially for adolescents, informal employment might have become an attractive alternative, for instance when the net income of informal work (in

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combination with social benefits) matches or exceeds income from formal employment. In addition, for those with a criminal record, informal work offers a way to work around job restrictions (Fletcher

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2008).

Overall, the gender-specific models showed comparable effect sizes and replicated the main

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findings: years spent (partly) unemployed are riskier than years spent employed and certain states of non-participation are positively (student) or negatively associated with crime (parental leave). Labour market status seems to affect crime across gender lines. In contrast to previous studies which mostly used a dichotomous employment measure, we find that changes in labour market status are not more

9

Receipt of social assistance seems most common among those who have no employment months but some unemployment months (over 60% receiving social assistance at the oldest ages), whereas those who have neither employment nor unemployment months receive social assistance less often (about 20% at the same ages).

19

important for understanding changes in male than female offending rates (Rodermond et al. 2016), at least in Finland where differences between men and women in employment rates are comparatively small. This finding aligns the theoretical perspective suggesting that female and male crime can be accounted for by the same factors (Smith and Paternoster 1987). Yet, the importance of the separate non-employment categories seems to differ somewhat for men and women, indicating that a gendered approach could improve our understanding of the underlying mechanisms (Steffensmeier and Allan 1996). For example, both staying outside the labour market without legitimate reasons and receipt of disability pension is related to a crime-increase only among women. Interestingly, Verbruggen et al.

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(2015) also found an increased crime risk among female receivers of a disability pension using Dutch data. For men, the qualitatively different non-employment categories seem relatively more important in predicting the probability of violent crime, whereas the doses of (un)employment were more

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important for women in all models. Spending time as a student and being non-participant without

legitimate reasons decreases violent crime whereas it increased (student) or did not affect property

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crime (non-participant). Thus far, few studies distinguished between these non-employment categories and crime types and therefore caution is advised in generalizing these findings to other countries.

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By comparing the estimates across crime type and for men and women separately, we were able to get insight into the types of crime that drive the results among these two groups. Both property

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crime models showed stronger effects than the omnibus- and violent crime models and replicated the pattern found with the omnibus measure. The weaker associations for violent crime are in line with

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some prior Nordic studies (XXXX 2013, Sariaslan et al. 2017). While this finding does not exclude other theoretical explanations for crime, it could imply that both men and women commit crimes

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during periods of joblessness to address financial needs. It also suggests that the social benefits in Finland, though more generous than in most other countries, do not eliminate the instrumental motivation for crime (see Verbruggen et al. 2015 for a similar finding in the Netherlands). The results indicate that the most crime-prone individuals are not completely inactive, but sign up as job seekers. In some ways this can be regarded as good news, at least if the other option is complete inactivity in terms of the formal labour market. It could mean that the crime-prone individuals in this representative sample of adolescents are willing to work a legal job but are unable 20

to find or hold down good, stable employment. While unwillingness to work is difficult to address through social policy, our finding suggests that policy that increases formal job opportunities could help to reduce crime among adolescents. At the same time, being registered as a job seeker does not necessarily always signal a strong intention of finding work, and could instead be done just to retain eligibility for benefits. This study adds to existing work that uses broad and short-term measures on high risk groups, but it remains difficult to reliably measure the actual labour market status of youth due to the transitional phase they are in. We decided to use categories that represent a continuum rather than

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discrete differences in which employment was the overriding criterion, but there are alternative ways to examine these relationships. One of which is to control for different statuses (studying,

employment, unemployment, etc.) simultaneously. Another option is to examine stability in labour

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market status. Do they move in and out of work (sporadic work histories), do they move between

different types of non-employment or is there more continuity? Another recommendation for future

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studies is to use shorter periods of observation to get insight into status dynamics – a year might be a rather long aggregate in the “chaotic” lives that some of the most crime-prone young individuals live

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(Sugie 2018), and thus mask important variation.

In sum, the findings suggest that the link between labour market status and crime is more

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complex than previously assumed. Attention was drawn to the possible bias in work-crime studies resulting from using an employment dichotomy in which heterogeneous categories of employment and

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non-employment were grouped together. Both for men and women, the available evidence seems to point out that employment is inversely linked to all crime measures considered, albeit to a different

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extent. In addition, qualitatively different categories of non-employment seem differently associated with crime. This study therefore advocates for a clearer conceptualization of labour market status in research that follows the changed nature of employment and better approximates the diversity and dynamic of the labour market as experienced by youth.

21

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Becker, G.S. (1968), ‘Crime and Punishment: An Economic Approach’, The Journal of Political Economy, 76:169–217.

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Bennett, P. and Ouazad, A. (2016), ‘Job Displacement and Crime: Evidence from Danish Microdata’, INSEAD Working Paper No. 2016/55/EPS. http://dx.doi.org/10.2139/ssrn.2815312

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Bracha, A. and Burke, M.A. (2016), ‘Who Counts as Employed?: Informal Work, Employment Status, and Labor Market Slack’, FRB of Boston Working Paper No. 16-29. https://ssrn.com/abstract=2935535

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Caliendo, M., and Schmidl, R. (2016), ‘Youth Unemployment and Active Labor Market Policies in Europe, IZA Journal of Labor Policy, 5(1): 1-30.

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Carcillo, S., Fernández, R., Konings, S. and Minea, A. (2015), ‘NEET Youth in the Aftermath of the Crisis: Challenges and Policies’, OECD Social, Employment and Migration Working Papers, No. 164, OECD Publishing. http://dx.doi.org/10.1787/5js6363503f6-en. Cobbina, J.E., Huebner, B.M. and Berg, M.T. (2012), ‘Men, Women, and Postrelease Offending, Crime & Delinquency, 58(3): 331–361.

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Cohen, L.E. and Felson, M. (1979), ‘Social Change and Crime Rate Trends: A Routine Activity Approach’, American Sociological Review, 44:588–608.

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De Li, S. and MacKenzie, D.L. (2003), ‘The Gendered Effects of Adult Social Bonds on the Criminal Activities of Probationers’, Criminal Justice Review, 28: 278–298. Denver, M., Siwach, G., and Bushway, S. D. (2017), ‘A New Look at the Employment and Recidivism Relationship Through the Lens of a Criminal Background Check’, Criminology, 55(1): 174-204. Fallesen, P., Geerdsen, L. P., Imai, S., and Tranæs, T. (2018), ‘The Effect of Active Labor Market Policies on Crime: Incapacitation and Program Effects’, Labour Economics, 52: 263-286. Finnish Centre for Pensions (2017), https://www.etk.fi/wp-content/uploads/tilasto-suomenelakkeensaajista-2016.pdf 22

Fletcher, D.R. (2008). Offender in the post-industrial labour market: from the underclass to the undercaste? Policy and Politics, 26: 283-297. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford University Press. Hirschi, T. (1969). ‘A Control Theory of Delinquency’, Criminology Theory: Selected Classic Readings, 289-305. Kleck, G. and Jackson, D. (2016), ‘What Kind of Joblessness Affects Crime? A National Case– Control Study of Serious Property Crime’, Journal of Quantitative Criminology, 32(4): 489-513. Kruttschnitt, C. (2013), ‘Gender and Crime’, Annual Review of Sociology, 39: 291-308. Lorber, J. (2001), ‘Gender Inequality: Feminist Theories and Politics’, Los Angeles: Roxbury Pub.

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MacDonald, R. (1997), ‘Youth, the 'Underclass' and Social Exclusion’, Psychology Press. Makarios, M., Steiner, B. and Travis, L. F. (2010), ‘Examining the Predictors of Recidivism Among Men and Women Released from Prison in Ohio’, Criminal Justice and Behavior, 37(12): 1377-1391. Murphy, K. M. and Topel, R. (1997), ‘Unemployment and Nonemployment’, The American Economic Review, 87(2): 295-300.

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Rodermond, E., Kruttschnitt, C., Slotboom, A. M. and Bijleveld, C.C. (2016), ‘Female Desistance: A Review of the Literature’, European Journal of Criminology, 13(1): 3-28. Sampson, R.J. and Laub, J.H. (1993), ‘Crime in the Making: Pathways and Turning Points through Life’, Cambridge, MA: Harvard University Press

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Sariaslan, A., Larsson, H., Lichtenstein, P. and Fazel S. (2017), ‘Neighborhood Influences on Violent Reoffending Risk in Released Prisoners Diagnosed With Psychotic Disorders’, Schizophrenia Bulletin, 43(5): 1011-1020.

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Smith, D. A., & Paternoster, R. (1987), ‘The Gender Gap in Theories of Deviance: Issues and Evidence, Journal of Research in Crime and Delinquency, 24(2): 140-172. Statistics Finland (2014) Crime Nomenclature 2014. https://www.stat.fi/meta/luokitukset/rikokset/001-2014/index_en.html Statistics Finland (2018), ‘Official Statistics of Finland: Employment’, ISSN=2323-6825. Helsinki: Statistics Finland [referred: 25.6.2018]. Steffensmeier, D. and Allan, E. (1996), ‘Gender and Crime: Toward a Gendered Theory of Female Offending’, Annual Review of Sociology, 22(1): 459-487.

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Sugie, N.F. (2018), ‘Work as Foraging: A Smartphone Study of Job Search and Employment after Prison’, American Journal of Sociology, 123(5): 1453-1491. Uggen, C. (1999), ‘Ex-offenders and the Conformist Alternative: A Job Quality Model of Work and Crime’, Social Problems, 46:127–51. Uggen, C. (2000), ‘Work as a Turning Point in the Life Course of Criminals: A Duration Model of Age, Employment, and Recidivism’, American Sociological Review, 65: 529–46. Van den Berg, C.J. (2015). From Boys to Men: Explaining Juvenile Sex Offenders' Criminal Careers. Amsterdam: Vrije Universiteit.

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Wadsworth, T. (2006), ‘The Meaning of Work: Conceptualizing the Deterrent Effect of Employment on Crime Among Young Adults’, Sociological Perspectives, 49(3): 343-368.

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Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT press.

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Figure 1. Labour market status by age, men.

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Figure 2. Labour market status by age, women.

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Note to editor: These tables are to replace figure 1 and 2 in the text (in case you are unable to print the figures in colour, black/white versions of the figures are not informative because we distinguish many different categories). Table 1. Labor market status by age, men (%, N) 18 1.0 8.3 12.5 0.3 1.2

19 8.7 23.0 14.7 0.9 1.8

20 9.0 15.3 16.7 1.9 3.2

21 22.5 26.1 11.4 2.2 2.1

22 32.2 21.9 8.7 2.1 1.6

23 35.6 20.6 8.1 2.4 1.5

24 38.9 21.0 7.8 2.4 1.5

25 43.0 20.4 7.2 3.2 1.4

26 46.6 19.0 6.7 4.1 1.4

25 40.2 18.8 6.4 1.3 0.7

26 42.0 17.3 5.8 1.8 0.7

18 1.1 10.3 16.8 0.2 1.1

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Table 2. Labor market status by age, women (%, N)

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Employed 12 months Employed 6-11 months Employed <6 months Unemployed 6-12 months Unemployed <6 months Non-participant without leg. reasons 1.6 2.0 2.3 1.8 1.7 1.7 1.7 1.8 1.9 Student 74.4 31.0 23.3 28.6 27.5 25.5 21.3 16.7 13.0 Disability pension 0.6 0.7 1.0 1.2 1.4 1.6 1.8 1.9 2.0 Military service 0.2 17.0 27.0 3.0 1.1 0.6 0.3 0.2 0.2 Parental leave 0.0 0.1 0.5 1.0 1.7 2.3 3.1 4.1 5.2 N 161589 161371 161040 160683 160427 160183 159848 159479 158993

19 13.2 26.8 17.3 0.5 1.5

20 23.6 23.7 11.1 0.8 1.2

21 29.1 21.2 9.2 0.8 0.8

22 32.2 19.9 8.8 0.7 0.7

23 34.7 20.3 7.8 0.8 0.6

24 37.8 19.8 7.0 0.9 0.7

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Employed 12 months Employed 6-11 months Employed <6 months Unemployed 6-12 months Unemployed <6 months Non-participant without leg. reasons 1.2 1.3 1.2 1.0 1.0 1.1 1.2 1.3 1.4 Student 67.5 35.7 32.1 29.1 25.5 21.3 17.2 13.5 10.8 Disability pension 0.5 0.5 0.7 1.0 1.1 1.3 1.4 1.5 1.5 Military service 0.0 0.3 0.3 0.1 0.0 0.0 0.0 0.0 0.0 Parental leave 1.3 2.9 5.3 7.9 10.1 12.1 14.1 16.3 18.7 N 154518 154061 153596 153253 152952 152616 152204 151720 151186

26

Table 1. Descriptive statistics All crime %

All Property Violent crime crime crime mean % %

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na

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Variable % N Age 18 11.2 316 107 5.2 .13 2.2 1.3 19 11.2 315 432 5.9 .15 2.2 1.5 20 11.2 314 636 4.9 .13 2.0 1.3 21 11.1 313 936 4.8 .13 1.9 1.3 22 11.1 313 379 4.5 .13 1.7 1.2 23 11.1 312 799 4.1 .11 1.6 1.1 24 11.1 312 052 3.8 .11 1.5 1.0 25 11.0 311 199 4.8 .12 1.4 1.0 26 11.0 310 179 4.7 .11 1.3 0.8 Sex Male 51.2 1 443 613 7.3 .20 2.6 1.9 Female 48.8 1 376 106 2.0 .05 0.9 0.5 Labour market status Employed 12 months 27.2 766 980 3.2 .05 0.6 0.8 a Employed 6-11 months 19.6 553 865 4.7 .08 1.3 1.2 a Employed <6 months 10.2 288 635 6.5 .18 2.7 1.7 Unemployed 6-12 months 1.5 43 053 20.9 1.08 12.6 5.7 Unemployed <6 months 1.3 37 818 20.9 1.23 13.4 6.2 Non-participant without leg. reasons 1.5 42 766 9.2 .58 5.7 2.4 Student 28.6 807 612 4.0 .10 1.6 0.9 Disability pension 1.2 34 003 5.4 .18 2.9 1.3 Military service 2.9 81 159 4.1 .06 1.1 1.0 Parental leave 5.8 163 828 3.2 .07 1.3 0.8 a Employment is used as the “overriding” criterion for this dose: non-employed months can consist of unemployment, studying or being outside the labour force. The unemployed/non-participation doses do not include employed months.

27

Table 2. Fixed-effects models, labour market status and all crime. Regression coefficients and clusterrobust standard errors from linear probability models. b 0.002 0.005

Se 0.0004 0.0005

sig. *** ***

Unemployed 6-12 months

0.020

0.0018

***

Unemployed <6 months

0.022

0.0018

***

Non-participant without leg. reasons -0.001

0.0016

ns.

Student

0.003

0.0004

***

Disability pension

0.001

0.0025

ns.

-0.008

0.0008

***

-0.013 0.008 -0.001

0.0006 0.0005 0.0005

*** *** **

-0.003

0.0005

***

-0.006

0.0005

***

-0.009

0.0005

***

-0.012

0.0005

***

-0.002 -0.005

0.0006 0.0006

** ***

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Labor market status Employed 6-11 months (ref. Employed 12 months) Employed <6 months

Military service

Age (ref. 18)

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Parental leave 19 20 21

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22

24

na

25 26

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23

317 320

NxT

2 819 719

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N

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Table 3. Gender-specific fixed-effects models, labour market status and all crime. Regression coefficients and cluster-robust standard errors from linear probability models. Coefficients for age dummies omitted from the table. Men

Women se 0.0007 0.0009 0.0023 0.0023 0.0022 0.0007 0.0039 0.0010 0.0016

sig. * *** *** *** ** * ns. *** ***

b 0.002 0.004 0.031 0.029 0.007 0.003 0.012 0.000 -0.013 155 120 1 376 106

se 0.0003 0.0005 0.0029 0.0026 0.0019 0.0004 0.0029 0.0035 0.0006

sig. *** *** *** *** *** *** *** ns. ***

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b 0.001 0.005 0.016 0.018 -0.007 0.002 -0.006 -0.014 -0.021 162 200 1 443 613

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Labour market status (ref. Employed 12 months) Employed 6-11 months Employed <6 months Unemployed 6-12 months Unemployed <6 months Non-participant without leg. reasons Student Disability pension Military service Parental leave N NxT

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Table 4. Gender-specific fixed-effects models, labour market status and property crime. Regression coefficients and cluster-robust standard errors from linear probability models. Coefficients for age dummies omitted from the table. Men

Women se 0.000 0.001 0.002 0.002 0.002 0.000 0.003 0.001 0.001

sig. ns. *** *** *** ns. *** ns. *** ***

b 0.001 0.003 0.025 0.023 0.010 0.002 0.010 0.000 -0.005 155 120 1 376 106

se 0.0002 0.0003 0.0023 0.0021 0.0016 0.0002 0.0025 0.0015 0.0004

sig. *** *** *** *** *** *** *** ns. ***

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b 0.000 0.005 0.021 0.022 0.000 0.002 -0.001 -0.004 -0.008 162 200 1 443 613

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Labour market status (ref. Employed 12 months) Employed 6-11 months Employed <6 months Unemployed 6-12 months Unemployed <6 months Non-participant without leg. reasons Student Disability pension Military service Parental leave N NxT

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Table 5. Gender-specific fixed-effects models, labour market status and violent crime. Regression coefficients and cluster-robust standard errors from linear probability models. Coefficients for age dummies omitted from the table. Men

Women se 0.0003 0.0005 0.0015 0.0016 0.0014 0.0004 0.0022 0.0005 0.0008

sig. ns. ns. * ** *** *** ** *** ***

b 0.001 0.001 0.007 0.005 0.002 0.001 0.003 0.002 -0.004 155 120 1 376 106

se 0.0002 0.0002 0.0016 0.0015 0.0010 0.0002 0.0015 0.0021 0.0003

sig. *** *** *** ** ns. *** ns. ns. ***

ro of

b 0.000 0.001 0.003 0.005 -0.008 -0.002 -0.006 -0.005 -0.007 162 200 1 443 613

Jo

ur

na

lP

re

-p

Labour market status (ref. Employed 12 months) Employed 6-11 months Employed <6 months Unemployed 6-12 months Unemployed <6 months Non-participant without leg. reasons Student Disability pension Military service Parental leave N NxT

31